A View-Agnostic Deep Learning Framework for Comprehensive Analysis of 2D-Echocardiography
Anisuzzaman, D. M.; Malins, J. G.; Jackson, J. I.; Lee, E.; Naser, J. A.; Rostami, B.; Bird, J. G.; Spiegelstein, D.; Amar, T.; Ngo, C. C.; Oh, J. K.; Pellikka, P. A.; Thaden, J. J.; Lopez-Jimenez, F.; Poterucha, T. J.; Friedman, P. A.; Pislaru, S.; Kane, G. C.; Attia, Z. I.
Show abstract
Echocardiography traditionally requires experienced operators to select and interpret clips from specific viewing angles. Clinical decision-making is therefore limited for handheld cardiac ultrasound (HCU), which is often collected by novice users. In this study, we developed a view-agnostic deep learning framework to estimate left ventricular ejection fraction (LVEF), patient age, and patient sex from any of several views containing the left ventricle. Model performance was: (1) consistently strong across retrospective transthoracic echocardiography (TTE) datasets; (2) comparable between prospective HCU versus TTE (625 patients; LVEF r2 0.80 vs. 0.86, LVEF [> or [≤]40%] AUC 0.981 vs. 0.993, age r2 0.85 vs. 0.87, sex classification AUC 0.985 vs. 0.996); (3) comparable between prospective HCU data collected by experts versus novice users (100 patients; LVEF r2 0.78 vs. 0.66, LVEF AUC 0.982 vs. 0.966). This approach may broaden the clinical utility of echocardiography by lessening the need for user expertise in image acquisition.
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